In this article, I am going to lay out the case that the biggest challenge to supply chains is data interoperability. In fact, I’ll be so bold as to call this massive and continuing failure of data interoperability, a supply chain nightmare! Just think about it. The key function of any supply chain is to get the right product to the right place at the right time. To do this paramount supply chain function, you need to have data that is accurate, complete, and timely. So, it is impossible for supply chains to work well when there is a lack of data interoperability between different systems and partners.
However, it is not time to give up hope. In this article, I’ll give you a short description of what supply chain data interoperability is and identify for you its many benefits. What’s more, I’ll describe relatable examples of what happens when a supply chain lacks data interoperability. Lastly, I’ll give you 6 examples of solutions that can go a long way in improving data interoperability in your supply chain. So, let’s unlock innovation in our supply chains!
“data interoperability is key for getting the right information to the right person at the right time”
What Is Data Interoperability And How Is It Important To Supply Chains?
Basically, data interoperability for logistics refers to the seamless exchange of information between different systems and platforms within a supply chain. Here is a basic definition for data interoperability:
“… the ability to access and process data from multiple sources without losing meaning and then integrate that data for mapping, visualization, and other forms of representation and analysis.”Global Partnership For Sustainable Development Data
Two Levels Of Data Interoperability
Further, there are two parts or stages to data interoperability. These are:
“Data-level Interoperability: Data-level or syntactic interoperability enables data to be shared across applications and platforms.
Semantic-Level Interoperability: This type of interoperability allows the data to be interpreted correctly…”AIMultiple
Now, it is important for business leaders to know that there are two parts to data interoperability. The key thing to understand is that the first level, data-level interoperability, just amounts to data transfer, and nothing else. Thus, this means that the “consuming” system can access and store the data from a source system. However, this system does not know how to extract meaning out of the data and turn the data into something actionable. Indeed, this is where the second step, semantic-level interoperability, comes in. This interoperability step is key to turn the data into information that supply chains can use.
Indeed, every supply chain organization needs data interoperability to succeed and stay competitive. So, even if a supply chain has just a few systems and supply chain partners it still needs data interoperability. Positively, data interoperability is the key for getting the right information to the right person at the right time. Moreover, interoperability allows data to flow smoothly between various software applications, systems, and devices. Thus, it enables the efficient integration of processes across the entire distribution network.
“And what happens when we want to exchange more than ‘selected, critical data’? The old mantra about the ‘right data, right person, right time’.”Heather Leslie
The Interoperability Benefits For Supply Chain Excellence And Innovation.
Successfully resolving data interoperability issues within supply chains has far-reaching implications for innovation potential. Indeed, it opens the door for advanced technologies like Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) to gain traction more effectively. Positively, these technologies can play an integral role in refining operational efficiency, cost optimization, risk mitigation, and overall business agility. Unlocking supply chain innovation truly begins with overcoming the data interoperability nightmare that plagues many industries today. Here are just a sampling of the many benefits of achieving data interoperability between your various supply chain systems.
Benefits Of Supply Chain Data Interoperability
- Able To Leverage Emerging Tech Such As AI and IoT
- Enhanced Collaboration Among Partners
- Improved Decision-Making and Forecasting
- Increased Operational Efficiency
- Reduced Lead Times and Downtime
- Better Inventory Management and Optimization
- Enhanced Customer Satisfaction and Loyalty
- Greater Visibility Across the Supply Chain
- Faster Decision-Making and Response Time
- Increased Adaptability to Market Changes
- Reduced Costs and Improved Efficiency
- Better Risk Management and Mitigation
- Enhanced Customer Services and Reliability
- Facilitated Regulatory Compliance and Sustainability
It is absolutely amazing the benefits that a supply chain can achieve with data interoperability. The bottom line is supply chain data interoperability enables supply chain staff to take action with quality supply chain data that is highly accurate, complete, and timely. Indeed, data interoperability enables the right data to get to the right person at the right time. This results in supply chain excellence and enables innovation to occur.
For more information on the benefits of data interoperability, see Tokenex’s What Is Interoperability and Why Is It Important?
Why Is Data Interoperability Such A Nightmare For Supply Chains?
Data interoperability poses numerous challenges particularly for supply chains because of the diverse range of technologies, hardware, and software platforms used by different stakeholders. Few industries have to deal with so many systems, numerous stakeholders, and massive amounts of data. Worse, data updates and status in supply chain operations are by their nature fleeting adding even more challenges for decision-makers. Moreover, integrating these disparate systems is a monstrous task requires considerable time, effort, and resources.
Moreover, data standards often differ between organizations, making it difficult to establish a common framework for communication. Also, concerns about data privacy and security hinder the establishment of open data exchange channels. Consequently, these complexities turn data interoperability into a nightmare for supply chain managers who are constantly seeking efficient ways to streamline operations and improve services. To list, below are some examples to further detail why supply chain data interoperability is a nightmare.
10 Examples Of Poor Supply Chain Data Interoperability
1. Inconsistent Data Formats.
For example, one system may use CSV files while another uses XML, causing data translation issues. Worse, even if the data format is the same the data is not normalized where each system have a common understanding of the data content.
2. Lack Of Standardized Codes.
For instance, if one company uses UPC codes while another uses EAN codes, it becomes challenging to accurately identify and track products across the supply chain.
3. Limited Data Sharing Capabilities.
In this case, if a supplier’s system does not allow direct integration with a customer’s system, it can lead to delays and errors in data exchange.
4. Manual Data Entry And Reconciliation.
For example, if a warehouse worker needs to manually enter shipment details into multiple systems, it increases the risk of errors and delays.
5. Lack Of Real-Time Visibility.
Poor supply chain data interoperability will result in a lack of real-time visibility into inventory levels, order status, or transportation updates. This will lead to inefficiencies and disruptions in the supply chain.
6. Incomplete Or Inaccurate Data.
To illustrate, if a supplier fails to update product specifications in their system, it can lead to incorrect information being shared across the supply chain.
7. Non-Standardized Naming Conventions.
For example, if one company refers to a product as “Widget A” while another refers to it as “Product 123”, it can lead to miscommunication and errors.
8. Lack of Data Validation Checks.
For instance, if a system does not validate the accuracy and completeness of incoming data, it can result in the propagation of errors throughout the supply chain.
9. Incompatible Data Integration Tools.
In this case, if one company uses an API-based integration tool while another uses a file-based integration tool, it will hinder seamless data exchange.
10. Proprietary System Lock-In.
For example, a shipping system has a proprietary data interface . Thus, it makes it difficult for a shipper to onboard new parcel carriers because it can’t print shipping labels for any new carriers that are not compatible with the shipping system.
“Interoperability enables us to seamlessly move data, and more importantly insight, between various systems.”Amy Waldron, Google Cloud
6 Solutions To Achieve Better Supply Chain Data Interoperability And Unlock Innovation.
There are many solutions available to address the challenges of data interoperability in supply chains. However, it is a challenge for many businesses on where to start. First, there is the dynamic nature of supply chains coupled with the staggering amount of technology solutions out there. Additionally, many supply chains are already chained to legacy systems that have created data silos and can contain many partial copies of the same data. Worse, this data is inaccurate, incomplete, and out-of-date. Well, welcome to modern supply chains!
The good news is that many of your competitors are in this same data interoperability nightmare. So, the answer is not to do nothing! Indeed, it’s time for you to get more data savvy and focus on taking steps, some big and some small, to improve your supply chain’s data interoperability. To detail, below are examples and references to improve your data interoperability.
Examples Of Solutions To Improve Supply Chain Data Interoperability
1. Standardized Data Formats for Increased Compatibility.
One major advantage of standardized data formats is that your data sources are not locked into proprietary software apps that will eventually become obsolete. Using a standardized data format is key to achieving data interoperability. Also, there are many supply chain-centric data formats that are governed by both U.S. and International data standards organizations.
For example of data standards organizations, the ASTM International technical committee F49 is focused on standardizing digital information in the supply chain. Also, an associated initiative, the Transport Unit IDentifier (TUID) Working Group, has a standardized solution for eliminating phantom loads on freight load boards. More data standardization examples include Electronic Data Interchange (EDI) for exchanging electronic documents. Also, the GS1 Global Data Synchronization Network (GS1 GDSN), allows different supply chain systems to exchange product information seamlessly, regardless of the software they use.
2. Collaborative Data Sharing Platforms Independent Of Software.
Utilizing collaborative data sharing platforms enables supply chain stakeholders to securely share and verify data in real-time, regardless of the software applications they use. To detail, there are three ways businesses can share data using these platforms.
First, there are content collaboration tools like Google Drive where individuals can share content or that businesses can use to semi-automate the sharing of files. See, Gartner’s Content Collaboration Tools – Reviews And Rating for these types of collaborative data sharing platforms.
Second, there are enterprise-level data collaboration platforms. These platforms are designed to simplify, automate, and centralize the collaboration and sharing of data assets. See Narrative’s What is a Data Collaboration Platform? for more information on what these platforms can do for an organization. Also, see Slashdot for a listing of different data collaboration platforms.
Third, for data-savvy businesses, you can build your own collaborative data sharing platform that is tailored to your business. For example, a business could use existing databases or data lakes to build a collaborative data sharing platform to share data across their enterprise and with trusted partners. Further, businesses could use existing data connections and APIs as well as use 3rd-party tools to extract needed source data. For example with parcel invoice data, Shiplab provides carrier billing data pipelines that enables businesses to easily extract and own their billing data. In turn, a business then could share this data with key stakeholders such as finance, operations, and even third-party providers such as freight bill auditors.
3. Use System Integration Interfaces Like Application Programming Interfaces (APIs).
By implementing APIs or other system integration interfaces, companies can integrate their supply chain management systems with external partners’ systems. Thus, enabling seamless data exchange and synchronization. For example, an e-commerce company can integrate its order management system with a logistics provider’s system using APIs to automatically update shipment status. For a more detailed discussion on data integration, see my article, The Best Ways To Access Data – Tech Solutions To Unlock Your Data Silos.
4. Use Automation Such As Robotic Process Automation (RPA) And AI to Streamline Processes.
Also, businesses can use traditional automation as well as emerging technologies to streamline data sourcing, data processing, and data optimization. Indeed, automation can eliminate repetitive manual tasks in the supply chain, such as data entry or order processing. By eliminating manual tasks, automation can also reduce data errors, multiple copies of data, and the timeliness of data availability.
For instance, robotic process automation (RPA) bots can extract relevant information from supplier invoices and automatically update inventory systems, reducing errors and improving data interoperability. For a more detailed discussion on how to leverage different types of business automation available today, see my article, Business Automation AI Remake: First Just Tech To Empower Processes And Now Operate Autonomously.
5. Strategic Partner Collaboration Platforms.
Implementing strategic partner collaboration platforms like Supplier Relationship Management (SRM) systems enables companies to collaborate closely with suppliers. This includes sharing critical data such as demand forecasts and production plans in real-time, leading to better supply chain coordination and data interoperability. Indeed, using software platforms like this can greatly enhance data sharing with partners for a particular supply chain function. However, the downside is that the data in these software vendor’s systems may not be available to other parts of the organization or for other use cases in the future.
Yes, make use of software like SRM, but make sure you have a way to keep ownership of the data for future use. For more ideas on taking a data-centric approach to supplier management, see my article, Supplier Management: Optimize, Make Compliant, Assure Quality, Mitigate Where Risky.
6. Leveraging Cloud-Based Solutions for Improved Accessibility.
Adopting cloud-based solutions allows supply chain stakeholders to access and share data from anywhere at any time. For example, a cloud-based inventory management system enables suppliers, manufacturers, and retailers to view real-time inventory levels, improving coordination and data interoperability across the supply chain network.
On the other hand, beware that any software-centric solution is likely to have a lock-in to your business data, possibly creating just another data silo. Additionally, you need to ask yourself if this software will help you to stay competitive in the future. Or, will it just turn into another legacy system in a couple of years that stifles any new innovative initiatives. Remember, software will come and go, but your data is permanent and a most valuable asset for your business. For more on the dangers of being application-centric, see my article, You Need To Think Data Centric To Be A Successful Business: Stop Being Data Driven, Application Centric.
Greetings! As an independent supply chain tech expert with 30+ years of hands-on experience, I take great pleasure in providing actionable insights to logistics leaders. My background includes implementing 100s of innovative solutions using emerging technologies and a data-centric development approach. I have also provided business intelligence (BI) solutions for 1,000s of shippers. For more about me, click here.